Bayesian information theoretic model-averaging stochastic item selection for computer adaptive testing: compromise-free item exposure.

The goal of Computer Adaptive Testing (CAT) is to reliably estimate an individual's ability as modeled by an item response theory (IRT) instrument using only a subset of the instrument's items. A secondary goal is to vary the items presented across different testing sessions so that the sequence of items does not become overly stereotypical -- we want all items to have an exposure rate sufficiently far from zero. We formulate the optimization problem for CAT in terms of Bayesian information theory, where one chooses the item at each step based on the criterion of the ability model discrepancy -- the statistical distance between the ability estimate at the next step and the full-test ability estimate. This viewpoint of CAT naturally motivates a stochastic selection procedure that equates choosing the next item to sampling from a model-averaging ensemble ability model. Using the NIH Work Disability Functional Assessment Battery (WD-FAB), we evaluate our new methods in comparison to pre-existing methods found in the literature. We find that our stochastic selector has superior properties in terms of both item exposure and test accuracy/efficiency.